Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.

examples/industrial_data_pretraining/sense_voice/demo_spk.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+"""
+SenseVoice with Speaker Diarization
+
+This demo shows how to use SenseVoice with VAD + Speaker Model + Punctuation Model
+to get per-sentence speaker labels.
+"""
+
+import torch
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+
+def main():
+    model_dir = "iic/SenseVoiceSmall"
+    device = (
+        "cuda:0"
+        if torch.cuda.is_available()
+        else "mps" if torch.backends.mps.is_available() else "cpu"
+    )
+
+    model = AutoModel(
+        model=model_dir,
+        vad_model="fsmn-vad",
+        vad_kwargs={"max_single_segment_time": 30000},
+        spk_model="cam++",
+        device=device,
+    )
+
+    wav_path = f"{model.model_path}/example/zh.mp3"
+    res = model.generate(
+        input=wav_path,
+        cache={},
+        language="auto",
+        use_itn=True,
+        batch_size_s=60,
+        merge_vad=True,
+        merge_length_s=15,
+    )
+
+    # Print full text
+    text = rich_transcription_postprocess(res[0]["text"])
+    print("Text:", text)
+    print()
+
+    # Print per-sentence results with speaker labels
+    print("Speaker Diarization Results:")
+    for sent in res[0]["sentence_info"]:
+        sent_text = rich_transcription_postprocess(sent["text"])
+        print(
+            f"  Speaker {sent['spk']}: [{sent['start']}ms - {sent['end']}ms] {sent_text}"
+        )
+
+
+if __name__ == "__main__":
+    main()